The approaches described in this section could be pursued, but are not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Increases in hardware and software support costs has given way to a new technology delivery model in which an application service provider hosts applications coupled to data storage units on networked devices that are owned by the application service provider. The application service provider's customers, typically business enterprises, connect to the hosted applications via a web browser and enter data via the applications with the expectation that the data entered will be available on-demand whenever needed. The customers typically access the data for various data mining or data aggregation operations required to perform various analytics, such as determining particular trends related to their enterprise's operations.
A common way of analyzing trends is to compare one or more elements to one or more other elements, and visualize what the relationship looks like. Specifically, a linear regression is a powerful tool in this context. A linear regression is an approach for modeling the relationship between a scalar response y variable, and one or more explanatory variables, x. Once determined, a linear regression may be used to forecast they variable as output in an accurate manner. However, linear regressions are computationally demanding to generate. Currently, pre-computing of linear regressions on pre-determined data sets is widespread. Pre-computing is necessary because of the large time and processing costs associated with existing solutions. Improved methods of generating linear regressions are needed.